Intelligent evaluation of sandstone rock structure based on a visual large model

Abstract

Existing sandstone rock structure evaluation methods rely on visual inspection, with low efficiency, semi-quantitative analysis of roundness, and inability to perform classified statistics in particle size analysis. This study presents an intelligent evaluation method for sandstone rock structure based on the Segment Anything Model (SAM). By developing a lightweight SAM fine-tuning method with rank-decomposition matrix adapters, a multispectral rock particle segmentation model named CoreSAM is constructed, which achieves rock particle edge extraction and type identification. Building upon this, we propose a comprehensive quantitative evaluation system for rock structure, assessing parameters including particle size, sorting, roundness, particle contact and cementation types. The experimental results demonstrate that CoreSAM outperforms existing methods in rock particle segmentation accuracy while showing excellent generalization across different image types such as CT scans and core photographs. The proposed method enables full-sample, classified particle size analysis and quantitative characterization of parameters like roundness, advancing reservoir evaluation towards more precise, quantitative, intuitive, and comprehensive development.